Este articulo es un Preprint
Los preprints son informes de investigación preliminares que no han sido certificados por revisión por pares. No deben considerarse para guiar la práctica clínica o los comportamientos relacionados con la salud y no deben publicarse en los medios como información establecida.
Los preprints publicados en línea permiten a los autores recibir comentarios rápidamente, y toda la comunidad científica puede evaluar de forma independiente el trabajo y responder adecuadamente. Estos comentarios se publican junto con los preprints para que cualquiera pueda leer y servir como una revisión pospublicación.
Early therapy with remdesivir and antibody combinations improves COVID-19 disease in mice
Preprint
en En
| PREPRINT-BIORXIV
| ID: ppbiorxiv-428478
ABSTRACT
Improving the standard of clinical care for individuals infected with SARS-CoV-2 variants is a global health priority. Small molecule antivirals like remdesivir (RDV) and biologics such as human monoclonal antibodies (mAb) have demonstrated therapeutic efficacy against SARS-CoV-2, the causative agent of COVID-19. However, it is not known if combination RDV/mAb will improve outcomes over single agent therapies or whether antibody therapies will remain efficacious against variants. In kinetic studies in a mouse-adapted model of ancestral SARS-CoV-2 pathogenesis, we show that a combination of two mAbs in clinical trials, C144 and C135, have potent antiviral effects against even when initiated 48 hours after infection. The same antibody combination was also effective in prevention and therapy against the B.1.351 variant of concern (VOC). Combining RDV and antibodies provided a modest improvement in outcomes compared to single agents. These data support the continued use of RDV to treat SARS-CoV-2 infections and support the continued clinical development of the C144 and C135 antibody combination to treat patients infected with SARS-CoV-2 variants.
cc_by_nc_nd
Texto completo:
1
Colección:
09-preprints
Base de datos:
PREPRINT-BIORXIV
Tipo de estudio:
Prognostic_studies
Idioma:
En
Año:
2021
Tipo del documento:
Preprint